app.py

import gradio as gr
import torch
import torchaudio
from speechbrain.pretrained import SpeakerRecognition
import torch.nn as nn
from transformers import AutoModel
import os
from huggingface_hub import hf_hub_download

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
speaker_model = SpeakerRecognition.from_hparams(
    source="speechbrain/spkrec-ecapa-voxceleb",
    savedir="tmp",
    run_opts={"device": device}
)

class PretrainedTransformerClassifier(nn.Module):
    def __init__(self, num_classes=3):
        super().__init__()
        self.transformer = AutoModel.from_pretrained('distilbert/distilroberta-base')
        for param in self.transformer.parameters():
            param.requires_grad = False
            
        for param in self.transformer.encoder.layer[-2:].parameters():
            param.requires_grad = True
        
        self.embed_projection = nn.Sequential(
            nn.Linear(1, 768),
            nn.LayerNorm(768),
            nn.Dropout(0.1)
        )
        
        self.classifier = nn.Sequential(
            nn.Linear(768, 256),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(256, num_classes)
        )

    def forward(self, x):
        x = self.embed_projection(x.unsqueeze(-1))
        if len(x.shape) == 2:
            x = x.unsqueeze(0)
            
        attention_mask = torch.ones((x.shape[0], x.shape[1])).to(x.device)
        transformer_output = self.transformer(
            inputs_embeds=x,
            attention_mask=attention_mask,
            return_dict=True
        )
        pooled_output = transformer_output.last_hidden_state[:, 0, :]
        return self.classifier(pooled_output)

# Load the model from Huggingface Hub
def load_model():
    model_path = hf_hub_download(repo_id="polygraf-ai/vexon-voice-authentication", filename="model.pth")
    config_path = hf_hub_download(repo_id="polygraf-ai/vexon-voice-authentication", filename="config.pth")
    
    config = torch.load(config_path)
    classifier = PretrainedTransformerClassifier(num_classes=config['num_classes']).to(device)
    classifier.load_state_dict(torch.load(model_path, map_location=device))
    classifier.eval()
    
    return classifier, config

classifier, model_config = load_model()

def extract_embedding(audio_path):
    try:
        signal, fs = torchaudio.load(audio_path)
        signal = signal.to(device)
        embedding = speaker_model.encode_batch(signal)
        return embedding.cpu().detach().numpy().flatten()
    except Exception as e:
        print(f"Error processing {audio_path}: {e}")
        return None

def verify_speaker(audio_path1, audio_path2):
    emb1 = extract_embedding(audio_path1)
    emb2 = extract_embedding(audio_path2)
    if emb1 is None or emb2 is None:
        return None
    
    tensor1 = torch.tensor(emb1).to(device)
    tensor2 = torch.tensor(emb2).to(device)
    similarity_score = torch.nn.functional.cosine_similarity(
        tensor1, tensor2, dim=0
    ).cpu().item()
    return similarity_score

def process_audio(audio1, audio2):
    """
    Process two audio files and return authentication results using the pretrained transformer classifier
    """
    if audio1 is None or audio2 is None:
        return "Please upload both audio files."
    
    temp_path1 = "temp_audio1.wav"
    temp_path2 = "temp_audio2.wav"
    
    try:
        torchaudio.save(temp_path1, 
                       torchaudio.load(audio1)[0], 
                       torchaudio.load(audio1)[1])
        torchaudio.save(temp_path2, 
                       torchaudio.load(audio2)[0], 
                       torchaudio.load(audio2)[1])
        
        score = verify_speaker(temp_path1, temp_path2)
        if score is None:
            return "Error processing audio files. Please ensure they are valid audio recordings."
        
        with torch.no_grad():
            score_tensor = torch.FloatTensor([[score]]).to(device)
            output = classifier(score_tensor)
            prediction = torch.argmax(output, dim=1).item()
            
        probabilities = torch.softmax(output, dim=1)[0]
        confidence = probabilities[prediction].item()
        
        label = "Original" if prediction == 0 else "Deepfake/Impersonation"
        
        result = f"""
        📊 Authentication Results:
        
        🔹 Similarity Score: {score:.4f}
        🔹 Classification: {label}
        🔹 Confidence: {confidence:.4f}
        
        {'⚠️ Potential Voice Impersonation Detected!' if prediction > 0 else '✅ Authentic Voice Match'}
        """

        return result
    
    except Exception as e:
        return f"An error occurred: {str(e)}"
    
    finally:
        if os.path.exists(temp_path1):
            os.remove(temp_path1)
        if os.path.exists(temp_path2):
            os.remove(temp_path2)

css = """
.gradio-container {
    font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
    color: white;
    border-radius: 8px;
    background: linear-gradient(to right, #2125ff, #4146ff);
    border: none;
    cursor: pointer;
}
.gr-button:hover {
    background: linear-gradient(to right, #1f23e6, #3b40e6);
}
.footer {
    margin-top: 20px;
    text-align: center;
    border-top: 1px solid #ccc;
    padding-top: 10px;
}
"""

demo = gr.Interface(
    fn=process_audio,
    inputs=[
        gr.Audio(label="Reference Voice Recording", type="filepath"),
        gr.Audio(label="Voice Recording to Verify", type="filepath")
    ],
    outputs=gr.Textbox(label="Authentication Results"),
    title="Vexon Voice Authentication",
    description="""
    Upload two voice recordings to verify if they are from the same person and detect potential voice impersonation attempts.
    
    The system uses a pretrained transformer model fine-tuned on voice similarity scores to:
    1. Calculate a similarity score between the recordings
    2. Classify the comparison as Real, DeepFake, or Impersonation
    3. Provide a confidence score for the classification
    
    Note: For best results, ensure recordings are clear and contain speech content.
    """,
    css=css
)

if __name__ == "__main__":
    demo.launch()